{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization Tutorial 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import required library functions\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load UN Canadian Immigration Data\n",
    "### The data is of yearly country wise immigration into Canada from around the globe between 1980 and 2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data, skip the top 20 and bottom 2 rows as they do not contain relevant data\n",
    "df_canada = pd.read_excel('data/canada.xlsx',\n",
    "                          sheet_name = 'Canada by Citizenship',\n",
    "                          skiprows = range(20),\n",
    "                          skipfooter = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Dimensions of the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>OdName</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>REG</th>\n",
       "      <th>RegName</th>\n",
       "      <th>DEV</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
       "      <td>5501</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>2978</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Albania</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>3616</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>909</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>957</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Type    Coverage          OdName  AREA AreaName   REG  \\\n",
       "0  Immigrants  Foreigners     Afghanistan   935     Asia  5501   \n",
       "1  Immigrants  Foreigners         Albania   908   Europe   925   \n",
       "2  Immigrants  Foreigners         Algeria   903   Africa   912   \n",
       "3  Immigrants  Foreigners  American Samoa   909  Oceania   957   \n",
       "4  Immigrants  Foreigners         Andorra   908   Europe   925   \n",
       "\n",
       "           RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n",
       "0    Southern Asia  902  Developing regions    16  ...  2978  3436  3009   \n",
       "1  Southern Europe  901   Developed regions     1  ...  1450  1223   856   \n",
       "2  Northern Africa  902  Developing regions    80  ...  3616  3626  4807   \n",
       "3        Polynesia  902  Developing regions     0  ...     0     0     1   \n",
       "4  Southern Europe  901   Developed regions     0  ...     0     0     1   \n",
       "\n",
       "   2007  2008  2009  2010  2011  2012  2013  \n",
       "0  2652  2111  1746  1758  2203  2635  2004  \n",
       "1   702   560   716   561   539   620   603  \n",
       "2  3623  4005  5393  4752  4325  3774  4331  \n",
       "3     0     0     0     0     0     0     0  \n",
       "4     1     0     0     0     0     1     1  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top 5 rows\n",
    "df_canada.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>OdName</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>REG</th>\n",
       "      <th>RegName</th>\n",
       "      <th>DEV</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
       "      <td>920</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1191</td>\n",
       "      <td>...</td>\n",
       "      <td>1816</td>\n",
       "      <td>1852</td>\n",
       "      <td>3153</td>\n",
       "      <td>2574</td>\n",
       "      <td>1784</td>\n",
       "      <td>2171</td>\n",
       "      <td>1942</td>\n",
       "      <td>1723</td>\n",
       "      <td>1731</td>\n",
       "      <td>2112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>192</td>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
       "      <td>922</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>124</td>\n",
       "      <td>161</td>\n",
       "      <td>140</td>\n",
       "      <td>122</td>\n",
       "      <td>133</td>\n",
       "      <td>128</td>\n",
       "      <td>211</td>\n",
       "      <td>160</td>\n",
       "      <td>174</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>193</td>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>910</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>91</td>\n",
       "      <td>77</td>\n",
       "      <td>71</td>\n",
       "      <td>64</td>\n",
       "      <td>60</td>\n",
       "      <td>102</td>\n",
       "      <td>69</td>\n",
       "      <td>46</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>194</td>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>910</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>615</td>\n",
       "      <td>454</td>\n",
       "      <td>663</td>\n",
       "      <td>611</td>\n",
       "      <td>508</td>\n",
       "      <td>494</td>\n",
       "      <td>434</td>\n",
       "      <td>437</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Type    Coverage          OdName  AREA AreaName  REG  \\\n",
       "190  Immigrants  Foreigners         Vietnam   935     Asia  920   \n",
       "191  Immigrants  Foreigners  Western Sahara   903   Africa  912   \n",
       "192  Immigrants  Foreigners           Yemen   935     Asia  922   \n",
       "193  Immigrants  Foreigners          Zambia   903   Africa  910   \n",
       "194  Immigrants  Foreigners        Zimbabwe   903   Africa  910   \n",
       "\n",
       "                RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n",
       "190  South-Eastern Asia  902  Developing regions  1191  ...  1816  1852  3153   \n",
       "191     Northern Africa  902  Developing regions     0  ...     0     0     1   \n",
       "192        Western Asia  902  Developing regions     1  ...   124   161   140   \n",
       "193      Eastern Africa  902  Developing regions    11  ...    56    91    77   \n",
       "194      Eastern Africa  902  Developing regions    72  ...  1450   615   454   \n",
       "\n",
       "     2007  2008  2009  2010  2011  2012  2013  \n",
       "190  2574  1784  2171  1942  1723  1731  2112  \n",
       "191     0     0     0     0     0     0     0  \n",
       "192   122   133   128   211   160   174   217  \n",
       "193    71    64    60   102    69    46    59  \n",
       "194   663   611   508   494   434   437   407  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bottom 5 rows\n",
    "df_canada.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Data columns (total 43 columns):\n",
      "Type        195 non-null object\n",
      "Coverage    195 non-null object\n",
      "OdName      195 non-null object\n",
      "AREA        195 non-null int64\n",
      "AreaName    195 non-null object\n",
      "REG         195 non-null int64\n",
      "RegName     195 non-null object\n",
      "DEV         195 non-null int64\n",
      "DevName     195 non-null object\n",
      "1980        195 non-null int64\n",
      "1981        195 non-null int64\n",
      "1982        195 non-null int64\n",
      "1983        195 non-null int64\n",
      "1984        195 non-null int64\n",
      "1985        195 non-null int64\n",
      "1986        195 non-null int64\n",
      "1987        195 non-null int64\n",
      "1988        195 non-null int64\n",
      "1989        195 non-null int64\n",
      "1990        195 non-null int64\n",
      "1991        195 non-null int64\n",
      "1992        195 non-null int64\n",
      "1993        195 non-null int64\n",
      "1994        195 non-null int64\n",
      "1995        195 non-null int64\n",
      "1996        195 non-null int64\n",
      "1997        195 non-null int64\n",
      "1998        195 non-null int64\n",
      "1999        195 non-null int64\n",
      "2000        195 non-null int64\n",
      "2001        195 non-null int64\n",
      "2002        195 non-null int64\n",
      "2003        195 non-null int64\n",
      "2004        195 non-null int64\n",
      "2005        195 non-null int64\n",
      "2006        195 non-null int64\n",
      "2007        195 non-null int64\n",
      "2008        195 non-null int64\n",
      "2009        195 non-null int64\n",
      "2010        195 non-null int64\n",
      "2011        195 non-null int64\n",
      "2012        195 non-null int64\n",
      "2013        195 non-null int64\n",
      "dtypes: int64(37), object(6)\n",
      "memory usage: 65.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# schema information\n",
    "df_canada.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 43)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# size of data frame\n",
    "df_canada.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean and Prepare the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conversion index and columns to lists\n",
    "#a = df_canada.columns.tolist()\n",
    "#b = df_canada.index.tolist()\n",
    "\n",
    "# remove unnecessary columns\n",
    "# in pandas axis=0 re|presents rows (default) and axis=1 represents columns.\n",
    "df_canada.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\n",
    "\n",
    "# rename some columns to make better sense\n",
    "df_canada.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert all column names to strings\n",
    "df_canada.columns = list(map(str, df_canada.columns))\n",
    "\n",
    "# full range of the time series\n",
    "years = list(map(str, range(1980, 2014)))\n",
    "\n",
    "# add Total column\n",
    "df_canada['Total'] = df_canada.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 39)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# size of data frame\n",
    "df_canada.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>340</td>\n",
       "      <td>...</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "      <td>58639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "      <td>15699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "      <td>69439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country Continent           Region             DevName  1980  1981  \\\n",
       "0     Afghanistan      Asia    Southern Asia  Developing regions    16    39   \n",
       "1         Albania    Europe  Southern Europe   Developed regions     1     0   \n",
       "2         Algeria    Africa  Northern Africa  Developing regions    80    67   \n",
       "3  American Samoa   Oceania        Polynesia  Developing regions     0     1   \n",
       "4         Andorra    Europe  Southern Europe   Developed regions     0     0   \n",
       "\n",
       "   1982  1983  1984  1985  ...  2005  2006  2007  2008  2009  2010  2011  \\\n",
       "0    39    47    71   340  ...  3436  3009  2652  2111  1746  1758  2203   \n",
       "1     0     0     0     0  ...  1223   856   702   560   716   561   539   \n",
       "2    71    69    63    44  ...  3626  4807  3623  4005  5393  4752  4325   \n",
       "3     0     0     0     0  ...     0     1     0     0     0     0     0   \n",
       "4     0     0     0     0  ...     0     1     1     0     0     0     0   \n",
       "\n",
       "   2012  2013  Total  \n",
       "0  2635  2004  58639  \n",
       "1   620   603  15699  \n",
       "2  3774  4331  69439  \n",
       "3     0     0      6  \n",
       "4     1     1     15  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_canada.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validate Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>508.394872</td>\n",
       "      <td>566.989744</td>\n",
       "      <td>534.723077</td>\n",
       "      <td>387.435897</td>\n",
       "      <td>376.497436</td>\n",
       "      <td>358.861538</td>\n",
       "      <td>441.271795</td>\n",
       "      <td>691.133333</td>\n",
       "      <td>714.389744</td>\n",
       "      <td>843.241026</td>\n",
       "      <td>...</td>\n",
       "      <td>1320.292308</td>\n",
       "      <td>1266.958974</td>\n",
       "      <td>1191.820513</td>\n",
       "      <td>1246.394872</td>\n",
       "      <td>1275.733333</td>\n",
       "      <td>1420.287179</td>\n",
       "      <td>1262.533333</td>\n",
       "      <td>1313.958974</td>\n",
       "      <td>1320.702564</td>\n",
       "      <td>32867.451282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>1949.588546</td>\n",
       "      <td>2152.643752</td>\n",
       "      <td>1866.997511</td>\n",
       "      <td>1204.333597</td>\n",
       "      <td>1198.246371</td>\n",
       "      <td>1079.309600</td>\n",
       "      <td>1225.576630</td>\n",
       "      <td>2109.205607</td>\n",
       "      <td>2443.606788</td>\n",
       "      <td>2555.048874</td>\n",
       "      <td>...</td>\n",
       "      <td>4425.957828</td>\n",
       "      <td>3926.717747</td>\n",
       "      <td>3443.542409</td>\n",
       "      <td>3694.573544</td>\n",
       "      <td>3829.630424</td>\n",
       "      <td>4462.946328</td>\n",
       "      <td>4030.084313</td>\n",
       "      <td>4247.555161</td>\n",
       "      <td>4237.951988</td>\n",
       "      <td>91785.498686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>952.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>5018.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>251.500000</td>\n",
       "      <td>295.500000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>434.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>508.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>832.000000</td>\n",
       "      <td>842.000000</td>\n",
       "      <td>899.000000</td>\n",
       "      <td>934.500000</td>\n",
       "      <td>888.000000</td>\n",
       "      <td>932.000000</td>\n",
       "      <td>772.000000</td>\n",
       "      <td>783.000000</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>22239.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>22045.000000</td>\n",
       "      <td>24796.000000</td>\n",
       "      <td>20620.000000</td>\n",
       "      <td>10015.000000</td>\n",
       "      <td>10170.000000</td>\n",
       "      <td>9564.000000</td>\n",
       "      <td>9470.000000</td>\n",
       "      <td>21337.000000</td>\n",
       "      <td>27359.000000</td>\n",
       "      <td>23795.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>42584.000000</td>\n",
       "      <td>33848.000000</td>\n",
       "      <td>28742.000000</td>\n",
       "      <td>30037.000000</td>\n",
       "      <td>29622.000000</td>\n",
       "      <td>38617.000000</td>\n",
       "      <td>36765.000000</td>\n",
       "      <td>34315.000000</td>\n",
       "      <td>34129.000000</td>\n",
       "      <td>691904.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               1980          1981          1982          1983          1984  \\\n",
       "count    195.000000    195.000000    195.000000    195.000000    195.000000   \n",
       "mean     508.394872    566.989744    534.723077    387.435897    376.497436   \n",
       "std     1949.588546   2152.643752   1866.997511   1204.333597   1198.246371   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%       13.000000     10.000000     11.000000     12.000000     13.000000   \n",
       "75%      251.500000    295.500000    275.000000    173.000000    181.000000   \n",
       "max    22045.000000  24796.000000  20620.000000  10015.000000  10170.000000   \n",
       "\n",
       "              1985         1986          1987          1988          1989  \\\n",
       "count   195.000000   195.000000    195.000000    195.000000    195.000000   \n",
       "mean    358.861538   441.271795    691.133333    714.389744    843.241026   \n",
       "std    1079.309600  1225.576630   2109.205607   2443.606788   2555.048874   \n",
       "min       0.000000     0.000000      0.000000      0.000000      0.000000   \n",
       "25%       0.000000     0.500000      0.500000      1.000000      1.000000   \n",
       "50%      17.000000    18.000000     26.000000     34.000000     44.000000   \n",
       "75%     197.000000   254.000000    434.000000    409.000000    508.500000   \n",
       "max    9564.000000  9470.000000  21337.000000  27359.000000  23795.000000   \n",
       "\n",
       "       ...          2005          2006          2007          2008  \\\n",
       "count  ...    195.000000    195.000000    195.000000    195.000000   \n",
       "mean   ...   1320.292308   1266.958974   1191.820513   1246.394872   \n",
       "std    ...   4425.957828   3926.717747   3443.542409   3694.573544   \n",
       "min    ...      0.000000      0.000000      0.000000      0.000000   \n",
       "25%    ...     28.500000     25.000000     31.000000     31.000000   \n",
       "50%    ...    210.000000    218.000000    198.000000    205.000000   \n",
       "75%    ...    832.000000    842.000000    899.000000    934.500000   \n",
       "max    ...  42584.000000  33848.000000  28742.000000  30037.000000   \n",
       "\n",
       "               2009          2010          2011          2012          2013  \\\n",
       "count    195.000000    195.000000    195.000000    195.000000    195.000000   \n",
       "mean    1275.733333   1420.287179   1262.533333   1313.958974   1320.702564   \n",
       "std     3829.630424   4462.946328   4030.084313   4247.555161   4237.951988   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%       36.000000     40.500000     37.500000     42.500000     45.000000   \n",
       "50%      214.000000    211.000000    179.000000    233.000000    213.000000   \n",
       "75%      888.000000    932.000000    772.000000    783.000000    796.000000   \n",
       "max    29622.000000  38617.000000  36765.000000  34315.000000  34129.000000   \n",
       "\n",
       "               Total  \n",
       "count     195.000000  \n",
       "mean    32867.451282  \n",
       "std     91785.498686  \n",
       "min         1.000000  \n",
       "25%       952.000000  \n",
       "50%      5018.000000  \n",
       "75%     22239.500000  \n",
       "max    691904.000000  \n",
       "\n",
       "[8 rows x 35 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for missing data\n",
    "df_canada.isnull().sum()\n",
    "\n",
    "# data numerical summary\n",
    "df_canada.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Index the Data\n",
    "### Enables lookup by country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# index data by country\n",
    "df_canada.set_index('Country', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continent                 Asia\n",
      "Region            Eastern Asia\n",
      "DevName      Developed regions\n",
      "1980                       701\n",
      "1981                       756\n",
      "1982                       598\n",
      "1983                       309\n",
      "1984                       246\n",
      "1985                       198\n",
      "1986                       248\n",
      "1987                       422\n",
      "1988                       324\n",
      "1989                       494\n",
      "1990                       379\n",
      "1991                       506\n",
      "1992                       605\n",
      "1993                       907\n",
      "1994                       956\n",
      "1995                       826\n",
      "1996                       994\n",
      "1997                       924\n",
      "1998                       897\n",
      "1999                      1083\n",
      "2000                      1010\n",
      "2001                      1092\n",
      "2002                       806\n",
      "2003                       817\n",
      "2004                       973\n",
      "2005                      1067\n",
      "2006                      1212\n",
      "2007                      1250\n",
      "2008                      1284\n",
      "2009                      1194\n",
      "2010                      1168\n",
      "2011                      1265\n",
      "2012                      1214\n",
      "2013                       982\n",
      "Total                    27707\n",
      "Name: Japan, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# print sample data\n",
    "print(df_canada.loc['Japan'])\n",
    "#print(df_canada.loc['Japan', 2000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Continuous Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize immigration from Haiti to Canada from 1980 to 2013."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot immigration pattern for Haiti\n",
    "df_canada.loc['Haiti', years].transpose().plot(kind = 'line')\n",
    "plt.title('Immigration from Haiti')\n",
    "plt.ylabel('Number of immigrants')\n",
    "plt.xlabel('Years')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize immigration from India and China to Canada from 1980 to 2013."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot immigration pattern for India and China\n",
    "df_IndiaChina = df_canada.loc[['India', 'China'], years]\n",
    "df_IndiaChina.transpose().plot(kind = 'line')\n",
    "plt.title('Immigration from India and China')\n",
    "plt.ylabel('Number of immigrants')\n",
    "plt.xlabel('Years')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Discrete Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize top 5 immigration contributions into Canada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "India             691904\n",
       "China             659962\n",
       "United Kingdom    551500\n",
       "Philippines       511391\n",
       "Pakistan          241600\n",
       "Name: Total, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort data by Total\n",
    "df_canada.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
    "\n",
    "# pick top 5 contibutors\n",
    "df_top5 = df_canada.head(5)['Total']\n",
    "\n",
    "# print data.\n",
    "df_top5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>India</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>8880</td>\n",
       "      <td>8670</td>\n",
       "      <td>8147</td>\n",
       "      <td>7338</td>\n",
       "      <td>5704</td>\n",
       "      <td>4211</td>\n",
       "      <td>7150</td>\n",
       "      <td>...</td>\n",
       "      <td>36210</td>\n",
       "      <td>33848</td>\n",
       "      <td>28742</td>\n",
       "      <td>28261</td>\n",
       "      <td>29456</td>\n",
       "      <td>34235</td>\n",
       "      <td>27509</td>\n",
       "      <td>30933</td>\n",
       "      <td>33087</td>\n",
       "      <td>691904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>5123</td>\n",
       "      <td>6682</td>\n",
       "      <td>3308</td>\n",
       "      <td>1863</td>\n",
       "      <td>1527</td>\n",
       "      <td>1816</td>\n",
       "      <td>1960</td>\n",
       "      <td>...</td>\n",
       "      <td>42584</td>\n",
       "      <td>33518</td>\n",
       "      <td>27642</td>\n",
       "      <td>30037</td>\n",
       "      <td>29622</td>\n",
       "      <td>30391</td>\n",
       "      <td>28502</td>\n",
       "      <td>33024</td>\n",
       "      <td>34129</td>\n",
       "      <td>659962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>22045</td>\n",
       "      <td>24796</td>\n",
       "      <td>20620</td>\n",
       "      <td>10015</td>\n",
       "      <td>10170</td>\n",
       "      <td>9564</td>\n",
       "      <td>9470</td>\n",
       "      <td>...</td>\n",
       "      <td>7258</td>\n",
       "      <td>7140</td>\n",
       "      <td>8216</td>\n",
       "      <td>8979</td>\n",
       "      <td>8876</td>\n",
       "      <td>8724</td>\n",
       "      <td>6204</td>\n",
       "      <td>6195</td>\n",
       "      <td>5827</td>\n",
       "      <td>551500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Philippines</td>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>6051</td>\n",
       "      <td>5921</td>\n",
       "      <td>5249</td>\n",
       "      <td>4562</td>\n",
       "      <td>3801</td>\n",
       "      <td>3150</td>\n",
       "      <td>4166</td>\n",
       "      <td>...</td>\n",
       "      <td>18139</td>\n",
       "      <td>18400</td>\n",
       "      <td>19837</td>\n",
       "      <td>24887</td>\n",
       "      <td>28573</td>\n",
       "      <td>38617</td>\n",
       "      <td>36765</td>\n",
       "      <td>34315</td>\n",
       "      <td>29544</td>\n",
       "      <td>511391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>978</td>\n",
       "      <td>972</td>\n",
       "      <td>1201</td>\n",
       "      <td>900</td>\n",
       "      <td>668</td>\n",
       "      <td>514</td>\n",
       "      <td>691</td>\n",
       "      <td>...</td>\n",
       "      <td>14314</td>\n",
       "      <td>13127</td>\n",
       "      <td>10124</td>\n",
       "      <td>8994</td>\n",
       "      <td>7217</td>\n",
       "      <td>6811</td>\n",
       "      <td>7468</td>\n",
       "      <td>11227</td>\n",
       "      <td>12603</td>\n",
       "      <td>241600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>San Marino</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New Caledonia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Melanesia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Marshall Islands</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Micronesia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Palau</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Micronesia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Continent              Region             DevName   1980  \\\n",
       "Country                                                                     \n",
       "India                 Asia       Southern Asia  Developing regions   8880   \n",
       "China                 Asia        Eastern Asia  Developing regions   5123   \n",
       "United Kingdom      Europe     Northern Europe   Developed regions  22045   \n",
       "Philippines           Asia  South-Eastern Asia  Developing regions   6051   \n",
       "Pakistan              Asia       Southern Asia  Developing regions    978   \n",
       "...                    ...                 ...                 ...    ...   \n",
       "San Marino          Europe     Southern Europe   Developed regions      1   \n",
       "New Caledonia      Oceania           Melanesia  Developing regions      0   \n",
       "Marshall Islands   Oceania          Micronesia  Developing regions      0   \n",
       "Western Sahara      Africa     Northern Africa  Developing regions      0   \n",
       "Palau              Oceania          Micronesia  Developing regions      0   \n",
       "\n",
       "                   1981   1982   1983   1984  1985  1986  ...   2005   2006  \\\n",
       "Country                                                   ...                 \n",
       "India              8670   8147   7338   5704  4211  7150  ...  36210  33848   \n",
       "China              6682   3308   1863   1527  1816  1960  ...  42584  33518   \n",
       "United Kingdom    24796  20620  10015  10170  9564  9470  ...   7258   7140   \n",
       "Philippines        5921   5249   4562   3801  3150  4166  ...  18139  18400   \n",
       "Pakistan            972   1201    900    668   514   691  ...  14314  13127   \n",
       "...                 ...    ...    ...    ...   ...   ...  ...    ...    ...   \n",
       "San Marino            0      0      0      0     0     1  ...      0      0   \n",
       "New Caledonia         0      0      0      0     0     0  ...      0      0   \n",
       "Marshall Islands      0      0      0      0     0     0  ...      0      0   \n",
       "Western Sahara        0      0      0      0     0     0  ...      0      1   \n",
       "Palau                 0      0      0      0     0     0  ...      0      0   \n",
       "\n",
       "                   2007   2008   2009   2010   2011   2012   2013   Total  \n",
       "Country                                                                    \n",
       "India             28742  28261  29456  34235  27509  30933  33087  691904  \n",
       "China             27642  30037  29622  30391  28502  33024  34129  659962  \n",
       "United Kingdom     8216   8979   8876   8724   6204   6195   5827  551500  \n",
       "Philippines       19837  24887  28573  38617  36765  34315  29544  511391  \n",
       "Pakistan          10124   8994   7217   6811   7468  11227  12603  241600  \n",
       "...                 ...    ...    ...    ...    ...    ...    ...     ...  \n",
       "San Marino            0      0      0      1      0      0      0       5  \n",
       "New Caledonia         0      1      0      0      0      0      2       5  \n",
       "Marshall Islands      2      0      0      0      0      0      0       2  \n",
       "Western Sahara        0      0      0      0      0      0      0       2  \n",
       "Palau                 1      0      0      0      0      0      0       1  \n",
       "\n",
       "[195 rows x 38 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_canada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot immigration pattern of top 5\n",
    "df_top5.transpose().plot(kind = 'bar')\n",
    "plt.title('Top 5 Immigration Contributors')\n",
    "plt.ylabel('Number of immigrants')\n",
    "plt.xlabel('Countries')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
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